{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ 17.45617089  10.01803925  -1.26079823]\n",
      "  stds:  [ 30.98649441  30.30016758  30.38133175]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [  5.55111512e-17   4.88498131e-17   5.55111512e-19]\n",
      "  std:  [ 0.99999999  0.99999999  0.99999999]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999998]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print 'Before batch normalization:'\n",
    "print '  means: ', a.mean(axis=0)\n",
    "print '  stds: ', a.std(axis=0)\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print 'After batch normalization (gamma=1, beta=0)'\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print '  mean: ', a_norm.mean(axis=0)\n",
    "print '  std: ', a_norm.std(axis=0)\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print 'After batch normalization (nontrivial gamma, beta)'\n",
    "print '  means: ', a_norm.mean(axis=0)\n",
    "print '  stds: ', a_norm.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [ 0.04360452  0.08205617  0.06480704]\n",
      "  stds:  [ 0.94740505  1.10336744  1.00616199]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in xrange(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print 'After batch normalization (test-time):'\n",
    "print '  means: ', a_norm.mean(axis=0)\n",
    "print '  stds: ', a_norm.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dx error:  9.97144849267e-09\n",
      "dgamma error:  8.04557062729e-11\n",
      "dbeta error:  3.6398051075e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dgamma error: ', rel_error(da_num, dgamma)\n",
    "print 'dbeta error: ', rel_error(db_num, dbeta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: You can still complete the rest of the assignment if you don't figure this part out, so don't worry too much if you can't get it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  1.22984815133e-11\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 4.21x\n"
     ]
    }
   ],
   "source": [
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print 'dx difference: ', rel_error(dx1, dx2)\n",
    "print 'dgamma difference: ', rel_error(dgamma1, dgamma2)\n",
    "print 'dbeta difference: ', rel_error(dbeta1, dbeta2)\n",
    "print 'speedup: %.2fx' % ((t2 - t1) / (t3 - t2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  11.4618903679\n",
      "W1 relative error: 4.38e-05\n",
      "W2 relative error: 1.14e-05\n",
      "W3 relative error: 1.32e-03\n",
      "b1 relative error: 8.88e-03\n",
      "b2 relative error: 8.88e-03\n",
      "b3 relative error: 4.53e-07\n",
      "beta1 relative error: 4.32e-09\n",
      "beta2 relative error: 1.43e-09\n",
      "gamma1 relative error: 3.93e-09\n",
      "gamma2 relative error: 2.77e-10\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  497.866239815\n",
      "W1 relative error: 2.56e-06\n",
      "W2 relative error: 8.98e-06\n",
      "W3 relative error: 8.64e-07\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 3.55e-07\n",
      "b3 relative error: 7.49e-07\n",
      "beta1 relative error: 7.19e-07\n",
      "beta2 relative error: 7.24e-08\n",
      "gamma1 relative error: 8.23e-07\n",
      "gamma2 relative error: 8.81e-08\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print 'Running check with reg = ', reg\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print 'Initial loss: ', loss\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))\n",
    "  if reg == 0: print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 10.186178\n",
      "(Epoch 0 / 10) train acc: 0.118000; val_acc: 0.102000\n",
      "(Epoch 1 / 10) train acc: 0.250000; val_acc: 0.217000\n",
      "(Epoch 2 / 10) train acc: 0.265000; val_acc: 0.234000\n",
      "(Epoch 3 / 10) train acc: 0.321000; val_acc: 0.234000\n",
      "(Epoch 4 / 10) train acc: 0.367000; val_acc: 0.259000\n",
      "(Epoch 5 / 10) train acc: 0.371000; val_acc: 0.275000\n",
      "(Epoch 6 / 10) train acc: 0.406000; val_acc: 0.259000\n",
      "(Epoch 7 / 10) train acc: 0.412000; val_acc: 0.287000\n",
      "(Epoch 8 / 10) train acc: 0.413000; val_acc: 0.287000\n",
      "(Epoch 9 / 10) train acc: 0.427000; val_acc: 0.291000\n",
      "(Epoch 10 / 10) train acc: 0.431000; val_acc: 0.285000\n",
      "(Iteration 1 / 200) loss: 3.137506\n",
      "(Epoch 0 / 10) train acc: 0.102000; val_acc: 0.087000\n",
      "(Epoch 1 / 10) train acc: 0.186000; val_acc: 0.178000\n",
      "(Epoch 2 / 10) train acc: 0.261000; val_acc: 0.224000\n",
      "(Epoch 3 / 10) train acc: 0.249000; val_acc: 0.223000\n",
      "(Epoch 4 / 10) train acc: 0.338000; val_acc: 0.263000\n",
      "(Epoch 5 / 10) train acc: 0.360000; val_acc: 0.286000\n",
      "(Epoch 6 / 10) train acc: 0.366000; val_acc: 0.249000\n",
      "(Epoch 7 / 10) train acc: 0.431000; val_acc: 0.291000\n",
      "(Epoch 8 / 10) train acc: 0.446000; val_acc: 0.296000\n",
      "(Epoch 9 / 10) train acc: 0.466000; val_acc: 0.286000\n",
      "(Epoch 10 / 10) train acc: 0.504000; val_acc: 0.296000\n"
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAN/CAYAAAB9YCF7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSTng/wPEOxac1t0rwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ce4d767d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print 'Running weight scale %d / %d' % (i + 1, len(weight_scales))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAODCAYAAADqx+bFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyP8DVga/D53HvCQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ce12a4650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer:\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
